Overview

Dataset statistics

Number of variables14
Number of observations738648
Missing cells66995
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory254.8 MiB
Average record size in memory361.7 B

Variable types

Text4
DateTime1
Numeric9

Alerts

active is highly overall correlated with recoveredHigh correlation
active_cases is highly overall correlated with confirmed and 1 other fieldsHigh correlation
case_fatality_ratio is highly overall correlated with tasa_letalidadHigh correlation
confirmed is highly overall correlated with active_cases and 1 other fieldsHigh correlation
deaths is highly overall correlated with active_cases and 1 other fieldsHigh correlation
recovered is highly overall correlated with activeHigh correlation
tasa_letalidad is highly overall correlated with case_fatality_ratioHigh correlation
province_state has 32936 (4.5%) missing valuesMissing
incident_rate has 16606 (2.2%) missing valuesMissing
case_fatality_ratio has 7981 (1.1%) missing valuesMissing
tasa_letalidad has 9271 (1.3%) missing valuesMissing
recovered is highly skewed (γ1 = 21.19230841)Skewed
active is highly skewed (γ1 = 53.82411329)Skewed
case_fatality_ratio is highly skewed (γ1 = 73.88402917)Skewed
tasa_letalidad is highly skewed (γ1 = 72.5558529)Skewed
confirmed has 9271 (1.3%) zerosZeros
deaths has 39184 (5.3%) zerosZeros
recovered has 679640 (92.0%) zerosZeros
active has 672394 (91.0%) zerosZeros
case_fatality_ratio has 32803 (4.4%) zerosZeros
active_cases has 10549 (1.4%) zerosZeros
tasa_letalidad has 31325 (4.2%) zerosZeros
daily_new has 8460 (1.1%) zerosZeros

Reproduction

Analysis started2025-11-30 04:29:26.846546
Analysis finished2025-11-30 04:30:16.316875
Duration49.47 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

province_state
Text

Missing 

Distinct598
Distinct (%)0.1%
Missing32936
Missing (%)4.5%
Memory size39.7 MiB
2025-11-30T04:30:16.573284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length44
Median length32
Mean length8.5296325
Min length3

Characters and Unicode

Total characters6019464
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAustralian Capital Territory
2nd rowNew South Wales
3rd rowNorthern Territory
4th rowQueensland
5th rowSouth Australia
ValueCountFrequency (%)
texas46920
 
5.5%
virginia34960
 
4.1%
georgia29624
 
3.5%
north28704
 
3.4%
carolina27232
 
3.2%
new24656
 
2.9%
kentucky22264
 
2.6%
dakota22264
 
2.6%
missouri21528
 
2.5%
south21344
 
2.5%
Other values (662)576202
67.3%
2025-11-30T04:30:16.968440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a803330
13.3%
i599038
 
10.0%
n483624
 
8.0%
o471848
 
7.8%
s455216
 
7.6%
e362322
 
6.0%
r320784
 
5.3%
t216936
 
3.6%
l207782
 
3.5%
149986
 
2.5%
Other values (50)1948598
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)6019464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a803330
13.3%
i599038
 
10.0%
n483624
 
8.0%
o471848
 
7.8%
s455216
 
7.6%
e362322
 
6.0%
r320784
 
5.3%
t216936
 
3.6%
l207782
 
3.5%
149986
 
2.5%
Other values (50)1948598
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6019464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a803330
13.3%
i599038
 
10.0%
n483624
 
8.0%
o471848
 
7.8%
s455216
 
7.6%
e362322
 
6.0%
r320784
 
5.3%
t216936
 
3.6%
l207782
 
3.5%
149986
 
2.5%
Other values (50)1948598
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6019464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a803330
13.3%
i599038
 
10.0%
n483624
 
8.0%
o471848
 
7.8%
s455216
 
7.6%
e362322
 
6.0%
r320784
 
5.3%
t216936
 
3.6%
l207782
 
3.5%
149986
 
2.5%
Other values (50)1948598
32.4%
Distinct201
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size42.9 MiB
2025-11-30T04:30:17.151471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length13
Mean length11.894727
Min length4

Characters and Unicode

Total characters8786016
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola
ValueCountFrequency (%)
united606720
44.8%
states603040
44.5%
russia15272
 
1.1%
japan9016
 
0.7%
india6808
 
0.5%
china6256
 
0.5%
colombia6256
 
0.5%
mexico6072
 
0.4%
ukraine5152
 
0.4%
brazil4968
 
0.4%
Other values (225)85928
 
6.3%
2025-11-30T04:30:17.462306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t1833040
20.9%
e1269744
14.5%
a751160
8.5%
i700744
 
8.0%
n680872
 
7.7%
s651984
 
7.4%
d635608
 
7.2%
616840
 
7.0%
S615920
 
7.0%
U612424
 
7.0%
Other values (50)417680
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)8786016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t1833040
20.9%
e1269744
14.5%
a751160
8.5%
i700744
 
8.0%
n680872
 
7.7%
s651984
 
7.4%
d635608
 
7.2%
616840
 
7.0%
S615920
 
7.0%
U612424
 
7.0%
Other values (50)417680
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8786016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t1833040
20.9%
e1269744
14.5%
a751160
8.5%
i700744
 
8.0%
n680872
 
7.7%
s651984
 
7.4%
d635608
 
7.2%
616840
 
7.0%
S615920
 
7.0%
U612424
 
7.0%
Other values (50)417680
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8786016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t1833040
20.9%
e1269744
14.5%
a751160
8.5%
i700744
 
8.0%
n680872
 
7.7%
s651984
 
7.4%
d635608
 
7.2%
616840
 
7.0%
S615920
 
7.0%
U612424
 
7.0%
Other values (50)417680
 
4.8%
Distinct189
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size28.2 MiB
Minimum2020-08-04 00:00:00
Maximum2021-11-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-30T04:30:17.603668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:17.751433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

confirmed
Real number (ℝ)

High correlation  Zeros 

Distinct114376
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50116.851
Minimum0
Maximum8032988
Zeros9271
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-11-30T04:30:17.908303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile210
Q11295
median3685
Q315071
95-th percentile157046.5
Maximum8032988
Range8032988
Interquartile range (IQR)13776

Descriptive statistics

Standard deviation283037.91
Coefficient of variation (CV)5.6475597
Kurtosis277.8474
Mean50116.851
Median Absolute Deviation (MAD)3016
Skewness14.851303
Sum3.7018712 × 1010
Variance8.0110457 × 1010
MonotonicityNot monotonic
2025-11-30T04:30:18.049816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09271
 
1.3%
1722
 
0.1%
4582
 
0.1%
20470
 
0.1%
13454
 
0.1%
391439
 
0.1%
54438
 
0.1%
39421
 
0.1%
9395
 
0.1%
327352
 
< 0.1%
Other values (114366)725104
98.2%
ValueCountFrequency (%)
09271
1.3%
1722
 
0.1%
2345
 
< 0.1%
3294
 
< 0.1%
4582
 
0.1%
563
 
< 0.1%
650
 
< 0.1%
757
 
< 0.1%
8285
 
< 0.1%
9395
 
0.1%
ValueCountFrequency (%)
80329881
< 0.1%
80090401
< 0.1%
79859441
< 0.1%
79615351
< 0.1%
79360071
< 0.1%
79091111
< 0.1%
78794681
< 0.1%
78518051
< 0.1%
78270131
< 0.1%
78007961
< 0.1%

deaths
Real number (ℝ)

High correlation  Zeros 

Distinct20436
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1058.3031
Minimum0
Maximum152002
Zeros39184
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-11-30T04:30:18.184889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q121
median63
Q3227
95-th percentile3938
Maximum152002
Range152002
Interquartile range (IQR)206

Descriptive statistics

Standard deviation6188.2375
Coefficient of variation (CV)5.8473208
Kurtosis269.93072
Mean1058.3031
Median Absolute Deviation (MAD)53
Skewness14.765748
Sum7.8171344 × 108
Variance38294283
MonotonicityNot monotonic
2025-11-30T04:30:18.634292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
039184
 
5.3%
29048
 
1.2%
38793
 
1.2%
78773
 
1.2%
18277
 
1.1%
48020
 
1.1%
117780
 
1.1%
147713
 
1.0%
67364
 
1.0%
127280
 
1.0%
Other values (20426)626416
84.8%
ValueCountFrequency (%)
039184
5.3%
18277
 
1.1%
29048
 
1.2%
38793
 
1.2%
48020
 
1.1%
56780
 
0.9%
67364
 
1.0%
78773
 
1.2%
86820
 
0.9%
96494
 
0.9%
ValueCountFrequency (%)
1520021
< 0.1%
1519751
< 0.1%
1519141
< 0.1%
1517981
< 0.1%
1516821
< 0.1%
1516231
< 0.1%
1515451
< 0.1%
1515441
< 0.1%
1515401
< 0.1%
1514711
< 0.1%

recovered
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct37057
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14847.761
Minimum0
Maximum6117560
Zeros679640
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-11-30T04:30:18.770482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile16609.9
Maximum6117560
Range6117560
Interquartile range (IQR)0

Descriptive statistics

Standard deviation149775.07
Coefficient of variation (CV)10.087384
Kurtosis595.68976
Mean14847.761
Median Absolute Deviation (MAD)0
Skewness21.192308
Sum1.0967269 × 1010
Variance2.2432573 × 1010
MonotonicityNot monotonic
2025-11-30T04:30:18.917033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0679640
92.0%
1241
 
< 0.1%
13199
 
< 0.1%
4196
 
< 0.1%
3176
 
< 0.1%
7103
 
< 0.1%
1063102
 
< 0.1%
183101
 
< 0.1%
163101
 
< 0.1%
699101
 
< 0.1%
Other values (37047)57688
 
7.8%
ValueCountFrequency (%)
0679640
92.0%
1241
 
< 0.1%
217
 
< 0.1%
3176
 
< 0.1%
4196
 
< 0.1%
55
 
< 0.1%
65
 
< 0.1%
7103
 
< 0.1%
897
 
< 0.1%
94
 
< 0.1%
ValueCountFrequency (%)
61175601
< 0.1%
61101241
< 0.1%
61033251
< 0.1%
60948961
< 0.1%
60907861
< 0.1%
60833191
< 0.1%
60758881
< 0.1%
60648561
< 0.1%
60587511
< 0.1%
60461061
< 0.1%

active
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct25777
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4082.9128
Minimum0
Maximum5658278
Zeros672394
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-11-30T04:30:19.042666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2037.65
Maximum5658278
Range5658278
Interquartile range (IQR)0

Descriptive statistics

Standard deviation81298.958
Coefficient of variation (CV)19.912
Kurtosis3241.7349
Mean4082.9128
Median Absolute Deviation (MAD)0
Skewness53.824113
Sum3.0158354 × 109
Variance6.6095206 × 109
MonotonicityNot monotonic
2025-11-30T04:30:19.181336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0672394
91.0%
1608
 
0.1%
2464
 
0.1%
9275
 
< 0.1%
3259
 
< 0.1%
4249
 
< 0.1%
6234
 
< 0.1%
10220
 
< 0.1%
8191
 
< 0.1%
7184
 
< 0.1%
Other values (25767)63570
 
8.6%
ValueCountFrequency (%)
0672394
91.0%
1608
 
0.1%
2464
 
0.1%
3259
 
< 0.1%
4249
 
< 0.1%
5154
 
< 0.1%
6234
 
< 0.1%
7184
 
< 0.1%
8191
 
< 0.1%
9275
 
< 0.1%
ValueCountFrequency (%)
56582781
< 0.1%
56313681
< 0.1%
56100461
< 0.1%
56100231
< 0.1%
55905721
< 0.1%
55675091
< 0.1%
55438041
< 0.1%
55191901
< 0.1%
54926991
< 0.1%
54663181
< 0.1%
Distinct4016
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size48.9 MiB
2025-11-30T04:30:19.458560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length45
Mean length20.425959
Min length4

Characters and Unicode

Total characters15087594
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola
ValueCountFrequency (%)
us603040
26.7%
texas47288
 
2.1%
virginia35144
 
1.6%
georgia29808
 
1.3%
north29440
 
1.3%
carolina27416
 
1.2%
new26680
 
1.2%
dakota22632
 
1.0%
kentucky22264
 
1.0%
south21896
 
1.0%
Other values (2758)1396612
61.7%
2025-11-30T04:30:19.910270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1523572
 
10.1%
a1382010
 
9.2%
,1308358
 
8.7%
n927248
 
6.1%
i926558
 
6.1%
o836030
 
5.5%
e834650
 
5.5%
s707664
 
4.7%
S702058
 
4.7%
r647384
 
4.3%
Other values (52)5292062
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)15087594
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1523572
 
10.1%
a1382010
 
9.2%
,1308358
 
8.7%
n927248
 
6.1%
i926558
 
6.1%
o836030
 
5.5%
e834650
 
5.5%
s707664
 
4.7%
S702058
 
4.7%
r647384
 
4.3%
Other values (52)5292062
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)15087594
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1523572
 
10.1%
a1382010
 
9.2%
,1308358
 
8.7%
n927248
 
6.1%
i926558
 
6.1%
o836030
 
5.5%
e834650
 
5.5%
s707664
 
4.7%
S702058
 
4.7%
r647384
 
4.3%
Other values (52)5292062
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)15087594
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1523572
 
10.1%
a1382010
 
9.2%
,1308358
 
8.7%
n927248
 
6.1%
i926558
 
6.1%
o836030
 
5.5%
e834650
 
5.5%
s707664
 
4.7%
S702058
 
4.7%
r647384
 
4.3%
Other values (52)5292062
35.1%

incident_rate
Real number (ℝ)

Missing 

Distinct438786
Distinct (%)60.8%
Missing16606
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean10143.356
Minimum0
Maximum54240.396
Zeros2539
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-11-30T04:30:20.041256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1009.6587
Q17534.2466
median10490.361
Q313043.302
95-th percentile17323.779
Maximum54240.396
Range54240.396
Interquartile range (IQR)5509.0552

Descriptive statistics

Standard deviation4665.947
Coefficient of variation (CV)0.4600003
Kurtosis1.1857012
Mean10143.356
Median Absolute Deviation (MAD)2725.5892
Skewness-0.014007373
Sum7.3239294 × 109
Variance21771062
MonotonicityNot monotonic
2025-11-30T04:30:20.175344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02539
 
0.3%
1.529597716184
 
< 0.1%
3.063453308184
 
< 0.1%
0.02741152929184
 
< 0.1%
70.65889419184
 
< 0.1%
0.8786188112184
 
< 0.1%
3337.453646184
 
< 0.1%
6.847790732184
 
< 0.1%
2.073530214182
 
< 0.1%
1.366680333179
 
< 0.1%
Other values (438776)717854
97.2%
(Missing)16606
 
2.2%
ValueCountFrequency (%)
02539
0.3%
0.02741152929184
 
< 0.1%
0.3038509564173
 
< 0.1%
0.32073156517
 
< 0.1%
0.37137339113
 
< 0.1%
0.3812028313171
 
< 0.1%
0.38379604792
 
< 0.1%
0.38638926443
 
< 0.1%
0.38825399981
 
< 0.1%
0.39676213062
 
< 0.1%
ValueCountFrequency (%)
54240.396083
< 0.1%
54222.059231
 
< 0.1%
54194.553961
 
< 0.1%
54148.711841
 
< 0.1%
54038.690751
 
< 0.1%
53662.785373
< 0.1%
53635.28011
 
< 0.1%
53158.522051
 
< 0.1%
52855.964061
 
< 0.1%
52846.795641
 
< 0.1%

case_fatality_ratio
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct304288
Distinct (%)41.6%
Missing7981
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean2.7689583
Minimum0
Maximum4965.3061
Zeros32803
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-11-30T04:30:20.316686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15943031
Q11.168485
median1.7021277
Q32.3841664
95-th percentile3.9056143
Maximum4965.3061
Range4965.3061
Interquartile range (IQR)1.2156815

Descriptive statistics

Standard deviation50.067837
Coefficient of variation (CV)18.081831
Kurtosis5908.6344
Mean2.7689583
Median Absolute Deviation (MAD)0.59101655
Skewness73.884029
Sum2023186.4
Variance2506.7883
MonotonicityNot monotonic
2025-11-30T04:30:20.455832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
032803
 
4.4%
1.886792453794
 
0.1%
1.960784314530
 
0.1%
1.818181818370
 
0.1%
1.923076923357
 
< 0.1%
1.785714286350
 
< 0.1%
3.703703704350
 
< 0.1%
1.538461538344
 
< 0.1%
3.157894737337
 
< 0.1%
2.5332
 
< 0.1%
Other values (304278)694100
94.0%
(Missing)7981
 
1.1%
ValueCountFrequency (%)
032803
4.4%
0.0070606509921
 
< 0.1%
0.0073104759121
 
< 0.1%
0.010257637673
 
< 0.1%
0.010282952581
 
< 0.1%
0.010309809781
 
< 0.1%
0.010338054381
 
< 0.1%
0.010401497821
 
< 0.1%
0.010442228373
 
< 0.1%
0.010460980541
 
< 0.1%
ValueCountFrequency (%)
4965.3061223
 
< 0.1%
48663
 
< 0.1%
4770.5882358
< 0.1%
4768.6274513
 
< 0.1%
4678.8461542
 
< 0.1%
4590.5660384
< 0.1%
4588.6792451
 
< 0.1%
4505.5555565
< 0.1%
4503.7037041
 
< 0.1%
4423.6363641
 
< 0.1%
Distinct184
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.4 MiB
2025-11-30T04:30:20.720427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters10341072
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row05-01-2021.csv
2nd row05-01-2021.csv
3rd row05-01-2021.csv
4th row05-01-2021.csv
5th row05-01-2021.csv
ValueCountFrequency (%)
10-23-2021.csv4016
 
0.5%
10-22-2021.csv4016
 
0.5%
10-18-2021.csv4016
 
0.5%
10-19-2021.csv4016
 
0.5%
10-20-2021.csv4016
 
0.5%
10-21-2021.csv4016
 
0.5%
10-31-2021.csv4016
 
0.5%
10-30-2021.csv4016
 
0.5%
10-08-2021.csv4016
 
0.5%
10-09-2021.csv4016
 
0.5%
Other values (174)698488
94.6%
2025-11-30T04:30:21.075705image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
21790424
17.3%
01766325
17.1%
-1477296
14.3%
11192319
11.5%
.738648
7.1%
c738648
7.1%
s738648
7.1%
v738648
7.1%
8196694
 
1.9%
7196694
 
1.9%
Other values (5)766728
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)10341072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21790424
17.3%
01766325
17.1%
-1477296
14.3%
11192319
11.5%
.738648
7.1%
c738648
7.1%
s738648
7.1%
v738648
7.1%
8196694
 
1.9%
7196694
 
1.9%
Other values (5)766728
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10341072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21790424
17.3%
01766325
17.1%
-1477296
14.3%
11192319
11.5%
.738648
7.1%
c738648
7.1%
s738648
7.1%
v738648
7.1%
8196694
 
1.9%
7196694
 
1.9%
Other values (5)766728
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10341072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21790424
17.3%
01766325
17.1%
-1477296
14.3%
11192319
11.5%
.738648
7.1%
c738648
7.1%
s738648
7.1%
v738648
7.1%
8196694
 
1.9%
7196694
 
1.9%
Other values (5)766728
7.4%

active_cases
Real number (ℝ)

High correlation  Zeros 

Distinct96560
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34210.787
Minimum-2270427
Maximum7962377
Zeros10549
Zeros (%)1.4%
Negative2422
Negative (%)0.3%
Memory size5.6 MiB
2025-11-30T04:30:21.207710image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2270427
5-th percentile116
Q11114
median3112
Q310777
95-th percentile101915.2
Maximum7962377
Range10232804
Interquartile range (IQR)9663

Descriptive statistics

Standard deviation233812.88
Coefficient of variation (CV)6.8344783
Kurtosis440.49832
Mean34210.787
Median Absolute Deviation (MAD)2543
Skewness18.534385
Sum2.5269729 × 1010
Variance5.4668464 × 1010
MonotonicityNot monotonic
2025-11-30T04:30:21.351624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010549
 
1.4%
11052
 
0.1%
2734
 
0.1%
3540
 
0.1%
21517
 
0.1%
9491
 
0.1%
39488
 
0.1%
4461
 
0.1%
54457
 
0.1%
100420
 
0.1%
Other values (96550)722939
97.9%
ValueCountFrequency (%)
-22704271
< 0.1%
-22574431
< 0.1%
-22418681
< 0.1%
-22265941
< 0.1%
-22158841
< 0.1%
-22041091
< 0.1%
-21924771
< 0.1%
-21812911
< 0.1%
-21710951
< 0.1%
-21601301
< 0.1%
ValueCountFrequency (%)
79623771
< 0.1%
79386301
< 0.1%
79157371
< 0.1%
78915371
< 0.1%
78662381
< 0.1%
78395521
< 0.1%
78101241
< 0.1%
77826931
< 0.1%
77580961
< 0.1%
77320961
< 0.1%

tasa_letalidad
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct308992
Distinct (%)42.4%
Missing9271
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean2.7409664
Minimum0
Maximum4975.5102
Zeros31325
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size5.6 MiB
2025-11-30T04:30:21.493004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.20491803
Q11.1710194
median1.7022942
Q32.372394
95-th percentile3.9136808
Maximum4975.5102
Range4975.5102
Interquartile range (IQR)1.2013746

Descriptive statistics

Standard deviation47.508877
Coefficient of variation (CV)17.332893
Kurtosis5727.8489
Mean2.7409664
Median Absolute Deviation (MAD)0.58448459
Skewness72.555853
Sum1999197.9
Variance2257.0934
MonotonicityNot monotonic
2025-11-30T04:30:21.717631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
031325
 
4.2%
1.886792453777
 
0.1%
1.960784314527
 
0.1%
2.325581395374
 
0.1%
1.818181818357
 
< 0.1%
1.785714286333
 
< 0.1%
3.225806452331
 
< 0.1%
1.538461538330
 
< 0.1%
2.5325
 
< 0.1%
1.351351351315
 
< 0.1%
Other values (308982)694383
94.0%
(Missing)9271
 
1.3%
ValueCountFrequency (%)
031325
4.2%
0.0068384251113
 
< 0.1%
0.0068553017191
 
< 0.1%
0.0068732065231
 
< 0.1%
0.0068920362521
 
< 0.1%
0.0069343318771
 
< 0.1%
0.0069614855813
 
< 0.1%
0.0069739870281
 
< 0.1%
0.0069867775241
 
< 0.1%
0.0070007175741
 
< 0.1%
ValueCountFrequency (%)
4975.5102041
 
< 0.1%
4852.9411763
< 0.1%
4819.6078431
 
< 0.1%
4726.9230771
 
< 0.1%
4639.6226422
< 0.1%
4637.7358491
 
< 0.1%
4615.094341
 
< 0.1%
4561.1111111
 
< 0.1%
4529.629631
 
< 0.1%
4505.5555562
< 0.1%

daily_new
Real number (ℝ)

Zeros 

Distinct145686
Distinct (%)19.7%
Missing201
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean60.355744
Minimum-7700684
Maximum7700753
Zeros8460
Zeros (%)1.1%
Negative353588
Negative (%)47.9%
Memory size5.6 MiB
2025-11-30T04:30:21.917492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7700684
5-th percentile-64886
Q1-3840
median26
Q33800
95-th percentile61573.9
Maximum7700753
Range15401437
Interquartile range (IQR)7640

Descriptive statistics

Standard deviation281990.82
Coefficient of variation (CV)4672.1455
Kurtosis311.63732
Mean60.355744
Median Absolute Deviation (MAD)3818
Skewness-1.2234156
Sum44569518
Variance7.9518822 × 1010
MonotonicityNot monotonic
2025-11-30T04:30:22.122756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08460
 
1.1%
1654
 
0.1%
2460
 
0.1%
13409
 
0.1%
3403
 
0.1%
14390
 
0.1%
4370
 
0.1%
6358
 
< 0.1%
5350
 
< 0.1%
49331
 
< 0.1%
Other values (145676)726262
98.3%
ValueCountFrequency (%)
-77006841
< 0.1%
-76695451
< 0.1%
-76338811
< 0.1%
-75974041
< 0.1%
-75643311
< 0.1%
-75268291
< 0.1%
-74949241
< 0.1%
-74620751
< 0.1%
-74300741
< 0.1%
-73888151
< 0.1%
ValueCountFrequency (%)
77007531
< 0.1%
76696141
< 0.1%
76339501
< 0.1%
75974731
< 0.1%
75644001
< 0.1%
75268981
< 0.1%
74949921
< 0.1%
74621441
< 0.1%
74301431
< 0.1%
73888841
< 0.1%

Interactions

2025-11-30T04:30:10.396503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:53.341287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:55.439595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:57.887054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:00.031795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:02.103448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:04.142600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:06.002607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:08.080058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:10.697691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:53.560492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:55.654154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:58.180996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:00.241447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:02.361275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:04.355617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:06.200373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:08.302011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:11.016586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:53.764781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:55.889068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:58.476821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:00.650373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:02.580315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:04.568609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:06.402353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:08.504361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:11.333414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:53.967477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:56.113744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:58.774494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:00.854871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:02.808635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:04.775009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:06.601944image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:08.706686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:11.594905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:54.360224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:56.450794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:58.967199image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:01.050859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:03.010183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:04.996295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:06.794244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:08.978644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:11.810637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:54.571977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:56.733060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:59.167893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:01.256051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:03.247502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:05.188085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:07.013898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:09.283775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:12.023269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:54.775424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:57.015355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:59.383305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:01.450031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:03.474411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:05.393582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:07.209081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:09.554741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:12.235178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:54.988716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:57.310247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:59.600919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:01.653856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:03.698591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:05.591220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:07.649056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:09.821920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:12.461287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:55.202124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:57.608877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:29:59.821606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:01.874065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:03.929927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:05.790599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:07.849243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-30T04:30:10.091486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-30T04:30:22.277281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
activeactive_casescase_fatality_ratioconfirmeddaily_newdeathsincident_raterecoveredtasa_letalidad
active1.0000.0040.0310.3170.0280.289-0.3580.8710.032
active_cases0.0041.000-0.0090.9060.4510.8310.098-0.101-0.015
case_fatality_ratio0.031-0.0091.0000.012-0.0800.2920.061-0.0020.996
confirmed0.3170.9060.0121.0000.4460.925-0.0360.2510.007
daily_new0.0280.451-0.0800.4461.0000.4040.0710.016-0.081
deaths0.2890.8310.2920.9250.4041.000-0.0080.2330.291
incident_rate-0.3580.0980.061-0.0360.071-0.0081.000-0.3540.053
recovered0.871-0.101-0.0020.2510.0160.233-0.3541.000-0.002
tasa_letalidad0.032-0.0150.9960.007-0.0810.2910.053-0.0021.000

Missing values

2025-11-30T04:30:12.847481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-30T04:30:13.831552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-30T04:30:15.316670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

province_statecountry_regionlast_updateconfirmeddeathsrecoveredactivecombined_keyincident_ratecase_fatality_ratiosource_fileactive_casestasa_letalidaddaily_new
0NaNAfghanistan2021-05-0259939263153272.04036.0Afghanistan153.9726554.38946305-01-2021.csv4036.04.389463NaN
1NaNAlbania2021-05-021311852396110172.018617.0Albania4558.5169231.82642805-01-2021.csv18617.01.826428NaN
2NaNAlgeria2021-05-02122311326185249.033801.0Algeria278.9238102.66615405-01-2021.csv33801.02.666154NaN
3NaNAndorra2021-05-021323212512684.0423.0Andorra17125.4772540.94468005-01-2021.csv423.00.944680NaN
4NaNAngola2021-05-022681560023913.02302.0Angola81.5882112.23755405-01-2021.csv2302.02.237554NaN
5NaNAntigua and Barbuda2021-05-021232321014.0186.0Antigua and Barbuda1258.0671512.59740305-01-2021.csv186.02.597403NaN
6NaNArgentina2021-05-022993865640962655359.0274410.0Argentina6624.2140282.14091105-01-2021.csv274410.02.140911NaN
7NaNArmenia2021-05-022165964128199115.013353.0Armenia7309.4463681.90585205-01-2021.csv13353.01.905852NaN
8Australian Capital TerritoryAustralia2021-05-021243120.01.0Australian Capital Territory, Australia28.9651952.41935505-01-2021.csv1.02.419355NaN
9New South WalesAustralia2021-05-025484540.05430.0New South Wales, Australia67.5535850.98468305-01-2021.csv5430.00.9846835360.0
province_statecountry_regionlast_updateconfirmeddeathsrecoveredactivecombined_keyincident_ratecase_fatality_ratiosource_fileactive_casestasa_letalidaddaily_new
738638NaNWinter Olympics 20222021-11-01000.00.0Winter Olympics 20220.00.010-31-2021.csv0.0NaN0.0
738639NaNAntarctica2021-11-01000.00.0Antarctica0.00.010-31-2021.csv0.0NaN0.0
738640JerseyUnited Kingdom2021-11-0111027800.00.0Jersey, United Kingdom0.00.010-31-2021.csv10947.00.725492-426945.0
738641GuernseyUnited Kingdom2021-11-012134230.00.0Guernsey, United Kingdom0.00.010-31-2021.csv2111.01.077788-8893.0
738642NaNKorea, North2021-11-01000.00.0Korea, North0.00.010-31-2021.csv0.0NaN0.0
738643UnknownUkraine2021-11-01000.00.0Unknown, Ukraine0.00.010-31-2021.csv0.0NaN-116394.0
738644NaNNauru2021-11-01000.00.0Nauru0.00.010-31-2021.csv0.0NaN0.0
738645NiueNew Zealand2021-11-01000.00.0Niue, New Zealand0.00.010-31-2021.csv0.0NaN-6594.0
738646NaNTuvalu2021-11-01000.00.0Tuvalu0.00.010-31-2021.csv0.0NaN0.0
738647Pitcairn IslandsUnited Kingdom2021-11-01000.00.0Pitcairn Islands, United Kingdom0.00.010-31-2021.csv0.0NaN-2134.0